Assessing the Control Systems Capacity for Demand Response in California Industries

Ghatikar, Girish ; McKane, Aimee ; Goli, Sasank ; Therkelsen, Peter ; Olsen, Daniel

Lawrence Berkeley National Laboratory

关键词: 32 Energy Conservation, Consumption, And Utilization Demand Response, Automated Dr, Openadr, Industrial Controls, Dynamic Pricing, Controls And Automation;    Demand Response, Automated Dr, Openadr, Industrial Controls, Dynamic Pricing, Controls And Automation;   

DOI  :  10.2172/1057569
RP-ID  :  LBNL-5319E
RP-ID  :  DE-AC02-05CH11231
RP-ID  :  1057569

美国|英语

来源: UNT Digital Library

【 摘 要 】

California's electricity markets are moving toward dynamic pricing models, such as real-time pricing, within the next few years, which could have a significant impact on an industrial facility's cost of energy use during the times of peak use. Adequate controls and automated systems that provide industrial facility managers real-time energy use and cost information are necessary for successful implementation of a comprehensive electricity strategy; however, little is known about the current control capacity of California industries. To address this gap, Lawrence Berkeley National Laboratory, in close collaboration with California industrial trade associations, conducted a survey to determine the current state of controls technologies in California industries. This,study identifies sectors that have the technical capability to implement Demand Response (DR) and Automated Demand Response (Auto-DR). In an effort to assist policy makers and industry in meeting the challenges of real-time pricing, facility operational and organizational factors were taken into consideration to generate recommendations on which sectors Demand Response efforts should be focused. Analysis of the survey responses showed that while the vast majority of industrial facilities have semi- or fully automated control systems, participation in Demand Response programs is still low due to perceived barriers. The results also showed that the facilities that use continuous processes are good Demand Response candidates. When comparing facilities participating in Demand Response to those not participating, several similarities and differences emerged. Demand Response-participating facilities and non-participating facilities had similar timings of peak energy use, production processes, and participation in energy audits. Though the survey sample was smaller than anticipated, the results seemed to support our preliminary assumptions. Demonstrations of Auto-Demand Response in industrial facilities with good control capabilities are needed to dispel perceived barriers to participation and to investigate industrial subsectors suggested of having inherent Demand Response potential.